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» Learning for Semantic Parsing
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SOFSEM
2000
Springer
14 years 16 days ago
Towards High Speed Grammar Induction on Large Text Corpora
Abstract. In this paper we describe an e cient and scalable implementation for grammar induction based on the EMILE approach ( 2], 3], 4], 5], 6]). The current EMILE 4.1 implementa...
Pieter W. Adriaans, Marten Trautwein, Marco Vervoo...
NIPS
2008
13 years 10 months ago
Partially Observed Maximum Entropy Discrimination Markov Networks
Learning graphical models with hidden variables can offer semantic insights to complex data and lead to salient structured predictors without relying on expensive, sometime unatta...
Jun Zhu, Eric P. Xing, Bo Zhang
COLING
2000
13 years 10 months ago
Word Sense Disambiguation of Adjectives Using Probabilistic Networks
In this paper, word sense dismnbiguation (WSD) accuracy achievable by a probabilistic classifier, using very milfimal training sets, is investigated. \Ve made the assuml)tiou that...
Gerald Chao, Michael G. Dyer
NIPS
1993
13 years 10 months ago
The Power of Amnesia
We propose a learning algorithm for a variable memory length Markov process. Human communication, whether given as text, handwriting, or speech, has multi characteristic time scal...
Dana Ron, Yoram Singer, Naftali Tishby
TKDE
2008
116views more  TKDE 2008»
13 years 9 months ago
Long-Term Cross-Session Relevance Feedback Using Virtual Features
Relevance feedback (RF) is an iterative process, which refines the retrievals by utilizing the user's feedback on previously retrieved results. Traditional RF techniques solel...
Peng-Yeng Yin, Bir Bhanu, Kuang-Cheng Chang, Anlei...